Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Protein complex prediction via cost-based clustering
Bioinformatics
The MIPS mammalian protein--protein interaction database
Bioinformatics
Local modeling of global interactome networks
Bioinformatics
Feature selection using principal feature analysis
Proceedings of the 15th international conference on Multimedia
Image classification using principal feature analysis
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
ProCope—protein complex prediction and evaluation
Bioinformatics
Introduction to Machine Learning
Introduction to Machine Learning
Bioinformatics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Protein complex prediction approaches are based on the assumptions that complexes have dense protein-protein interactions and high functional similarity between their subunits. We investigated those assumptions by studying the subunits' interaction topology, sequence similarity and molecular function for human and yeast protein complexes. Inclusion of amino acids' physicochemical properties can provide better understanding of protein complex properties. Principal component analysis is carried out to determine the major features. Adopting amino acid composition profile information with the SVM classifier serves as an effective post-processing step for complexes classification. Improvement is based on primary sequence information only, which is easy to obtain.